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Incorporating Emotion and Personality-Based Analysis in User-Centered Modelling

  • Mohamed Mostafa
  • Tom Crick
  • Ana C. Calderon
  • Giles Oatley
Conference paper

Abstract

Understanding complex user behaviour under various conditions, scenarios and journeys is fundamental to improving the user-experience for a given system. Predictive models of user reactions, responses—and in particular, emotions—can aid in the design of more intuitive and usable systems. Building on this theme, the preliminary research presented in this paper correlates events and interactions in an online social network against user behaviour, focusing on personality traits. Emotional context and tone is analysed and modelled based on varying types of sentiments that users express in their language using the IBM Watson Developer Cloud tools. The data collected in this study thus provides further evidence towards supporting the hypothesis that analysing and modelling emotions, sentiments and personality traits provides valuable insight into improving the user experience of complex social computer systems.

Keywords

Emotions Personality Sentiment analysis User experience Social networking Affective computing 

References

  1. 1.
    Blamey, B., Crick, T., Oatley, G.: R U :-) or :-( ? Character- vs. word-gram feature selection for sentiment classification of OSN Corpora. In: Research & Development in Intelligent Systems XXIX (2012)Google Scholar
  2. 2.
    Blamey, B., Crick, T., Oatley, G.: ‘The First Day of Summer’: parsing temporal expressions with distributed semantics. In: Research & Development in Intelligent Systems XXX (2013)Google Scholar
  3. 3.
    Fast, L.A., Funder, D.C.: Personality as manifest in word use: correlations with self-report, acquaintance report, and behavior J. Pers. Soc. Psychol. 94(2), 334–346 (2008)Google Scholar
  4. 4.
    Lambiotte, R., Kosinski, M.: Tracking the digital footprints of personality. Proc. IEEE 102(12), 1934–1939 (2014)CrossRefGoogle Scholar
  5. 5.
    Lazer, D., et al.: Computational social science. Science 323(5915), 721–723 (2009)CrossRefGoogle Scholar
  6. 6.
    Oatley, G., Crick, T.: Changing faces: identifying complex behavioural profiles. In: Proceedings of HAS 2014, LNCS, vol. 8533, pp. 282–293Google Scholar
  7. 7.
    Oatley, G., Crick, T.: Measuring UK crime gangs: a social network problem. Soc. Netw. Anal. Min. 5(1), 33 (2015)CrossRefGoogle Scholar
  8. 8.
    Oatley, G., Crick, T., Bolt, D.: CCTV as a smart sensor network. In: Proceedings of DASC 2015Google Scholar
  9. 9.
    Oatley, G., Crick, T., Mostafa, M.: Digital footprints: envisaging and analysing online behaviour. In: Proceedings of AISB Symposium 2015Google Scholar
  10. 10.
    Pennebaker, J., King, L.: Linguistic styles: language use as an individual difference. J. Pers. Soc. Psychol. 77(6), 1296–1312 (1999)CrossRefGoogle Scholar
  11. 11.
    Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic Inquiry and Word Count. Erlbaum Publishers (2001)Google Scholar
  12. 12.
    Schiaffino, S., Amandi, A.: Intelligent user profiling. In: Artificial Intelligence: An International Perspective, LNCS, vol. 5640, pp. 193–216 (2009)Google Scholar
  13. 13.
    Tausczik, Y.R., Pennebaker, J.W.: The Psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Mohamed Mostafa
    • 1
  • Tom Crick
    • 1
  • Ana C. Calderon
    • 1
  • Giles Oatley
    • 2
  1. 1.Department of Computing & Information SystemsCardiff Metropolitan UniversityCardiffUK
  2. 2.School of Engineering & Information TechnologyMurdoch UniversityMurdochAustralia

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